def get_config(self): config = { k: backend.eval(v) if tf_utils.is_tensor_or_variable(v) else v for k, v in self._fn_kwargs.items() } base_config = super().get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = {} if type(self) is MeanMetricWrapper: # pylint: disable=unidiomatic-typecheck # Only include function argument when the object is a MeanMetricWrapper # and not a subclass. config['fn'] = self._fn for k, v in self._fn_kwargs.items(): config[k] = backend.eval(v) if is_tensor_or_variable(v) else v base_config = super().get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = { k: backend.eval(v) if tf_utils.is_tensor_or_variable(v) else v for k, v in self._fn_kwargs.items() } if type(self) is MeanMetricWrapper: # Only include function argument when the object is a # MeanMetricWrapper and not a subclass. config["fn"] = self._fn base_config = super().get_config() return dict(list(base_config.items()) + list(config.items()))
def get_config(self): config = {} for k, v in self._fn_kwargs.items(): config[k] = backend.eval(v) if is_tensor_or_variable(v) else v base_config = super().get_config() return dict(list(base_config.items()) + list(config.items()))